Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Models

• URL: http://arxiv.org/abs/2506.14919v1
• Date: Tue, 17 Jun 2025 18:59:43 GMT
• Title: Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Models
• Authors: Xinkai Zhao, Yuta Tokuoka, Junichiro Iwasawa, Keita Oda,
• Abstract summary: We propose a Frequency-Calibrated Reconstruction Error (FCRE) method for MIAs on medical image diffusion models.<n>We analyze the reverse diffusion process, obtain the mid-frequency reconstruction error, and compute the structural similarity index score between the reconstructed and original images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing use of diffusion models for image generation, especially in sensitive areas like medical imaging, has raised significant privacy concerns. Membership Inference Attack (MIA) has emerged as a potential approach to determine if a specific image was used to train a diffusion model, thus quantifying privacy risks. Existing MIA methods often rely on diffusion reconstruction errors, where member images are expected to have lower reconstruction errors than non-member images. However, applying these methods directly to medical images faces challenges. Reconstruction error is influenced by inherent image difficulty, and diffusion models struggle with high-frequency detail reconstruction. To address these issues, we propose a Frequency-Calibrated Reconstruction Error (FCRE) method for MIAs on medical image diffusion models. By focusing on reconstruction errors within a specific mid-frequency range and excluding both high-frequency (difficult to reconstruct) and low-frequency (less informative) regions, our frequency-selective approach mitigates the confounding factor of inherent image difficulty. Specifically, we analyze the reverse diffusion process, obtain the mid-frequency reconstruction error, and compute the structural similarity index score between the reconstructed and original images. Membership is determined by comparing this score to a threshold. Experiments on several medical image datasets demonstrate that our FCRE method outperforms existing MIA methods.

Related papers

• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction [22.589087990596887]
  We focus on the underexplored task of magnitude-image-based MRI reconstruction.<n>Recent advancements in diffusion models, particularly denoising diffusion probabilistic models, have demonstrated strong capabilities in modeling image priors.<n>We propose a novel Self-Consistent Nested Diffusion Bridge (SC-NDB) framework that models accelerated MRI reconstruction.
  arXiv  Detail & Related papers  (2024-12-13T09:35:34Z)
• FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error [12.888629653445012]
  diffusion models struggle to accurately reconstruct mid-band frequency information in real images.<n>Fire-guided Reconstruction Error is first to investigate influence of frequency decomposition on reconstruction error.<n>Experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.
  arXiv  Detail & Related papers  (2024-12-10T03:02:34Z)
• Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting [7.379135816468852]
  Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI.<n> achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions.<n>We propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction.
  arXiv  Detail & Related papers  (2024-10-29T21:38:54Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method [2.626378252978696]
  We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
  arXiv  Detail & Related papers  (2024-02-17T13:09:00Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs [35.46541584018842]
  Unsupervised Anomaly Detection (UAD) aims to identify any anomaly as an outlier from a healthy training distribution.<n>generative models are used to learn the reconstruction of healthy brain anatomy for a given input image.<n>We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image.
  arXiv  Detail & Related papers  (2023-12-07T11:03:42Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction [7.347468593124183]
  We introduce a novel msEPI reconstruction approach called zero-MIRID (zero-shot self-supervised learning of Multi-shot Image Reconstruction for Improved Diffusion MRI) This method jointly reconstructs msEPI data by incorporating deep learning-based image regularization techniques. It achieves superior results compared to the state-of-the-art parallel imaging method, as demonstrated in an in-vivo experiment.
  arXiv  Detail & Related papers  (2023-08-09T17:54:56Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.